The rapid expansion of short-term rental platforms like
Airbnb has fundamentally transformed urban housing markets and tourism
economies. In Los Angeles County, this increase raises critical
questions about whether Airbnb increases airbnb solely increasing access
to tourism economy access or if it also reinforces existing patterns of
racial and economic inequality. This report analyzes Airbnb distribution
patterns across LA County, examining relationships between short-term
rental activity and neighborhood socioeconomic characteristics including
racial composition, median income, educational attainment, public
transit access, and violent crime. Drawing on Inside Airbnb listing data
(September 2025) and American Community Survey demographic indicators,
the analysis employs spatial analysis techniques to investigate whether
Airbnb concentrates in privileged neighborhoods, potentially
exacerbating displacement pressures documented by Wachsmuth and Weisler
(2018) in their rent gap framework. The findings reveal that racial
composition and income, rather than crime or transit access, primarily
correlate with where Airbnb activity concentrates and how it is priced,
with implications for equitable urban development policy.
The primary dataset comprises 45,886 Airbnb listings for
Los Angeles County obtained from Inside Airbnb (insideairbnb.com),
scraped in September 2025. Variables include price, coordinates, room
type, availability, and review metrics. Strengths include comprehensive
geographic coverage and precise geolocation enabling spatial analysis.
However, limitations include single snapshot temporal coverage
preventing longitudinal analysis, potential inactive or fraudulent
listings in user filled (crowd-sourced) data, and exclusion of unlisted
short term rentals operating outside Airbnb’s platform.
I
applied three filters to focus on active listings: removed entries with
missing or zero prices (likely delisted properties), filtered listings
above the 99th percentile ($2,573, retaining 99% while excluding
extremes reaching $85,000), and removed listings with zero availability
lacking reviews since 2024 (likely inactive). The final dataset
comprises 36,362 active listings.
Socioeconomic data derives
from the ACS 2019-2023 5-year estimates (2,498 LA County census tracts),
accessed via tidycensus and provided files. Variables include racial
composition (B03002), median income (B19013_001E), poverty (B17001),
education (B23006), and commuting patterns (B08006). Strengths include
the fact that it is government data with established methodology and
granular data on census tract geography. Limitations include 5-year
aggregation obscuring recent changes, margins of error in small
populations, and temporal mismatch with the 2025 Airbnb data.
All spatial data were transformed to EPSG:26911 (NAD83/UTM Zone 11N),
appropriate for LA County’s longitude range within UTM Zone 11. The
original census boundaries (EPSG:4269, unprojected) required
transformation for accurate distance calculations, particularly the 200
meter buffer analysis in Question 5. UTM Zone 11N provides minimal
regional distortion with metric units facilitating spatial operations.
Figure 1 maps listings by room type. Entire homes
demonstrate markedly higher density, clustering around centre, centre
south, and centre west areas, with dense coastal concentrations (Santa
Monica, Venice, Malibu) identified as Airbnb hotspots in prior research
(Sarkar, Koohikamali and Pick, 2017). Centre south (downtown LA) shows
substantial presence of both entire homes and other room types,
indicating the likely presence of both tourist and short duration
business travellers. Southern and eastern portions display sparse
presence across all types, revealing geographic inequalities in working
class neighborhoods in southeast of LA (Sarkar, Koohikamali and Pick,
2017).
Semi transparent symbology (alpha equals 0.2) creates
natural density encoding where darker concentrations indicate higher
volumes, while thin borders (0.3pt) follow cartographic best practices
for dense city scale point data.
Figure 2 employs Jenks
natural breaks classification, selected to reveal genuine price
discontinuities by maximizing within class homogeneity. An embedded
histogram illustrates the skewed price distribution (most listings below
$500), justifying Jenks over equal intervals. Highest prices ($772 to
$1,357) concentrate in centre west coastal communities, while lowest
prices ($18 to $147) dominate northern and southern inland areas. Gray
areas indicate census tracts without Airbnb listings, representing 9% of
LA County (227 tracts). Of these, 167 tracts genuinely lack Airbnb
presence, predominantly in southeastern LA and northern periphery,
revealing either systematic geographic exclusion from tourism economy
participation, or underreporting in those areas. An additional 45 tracts
are uninhabited areas with zero or very low populations which could
potentially constitute military installations or national forest areas
where demographic data might not be as well documented. The remaining 60
tracts had Airbnbs in the original dataset but all listings were removed
through outlier filtering, indicating these areas contained only extreme
priced properties. I chose to display missing data using gray shading
rather than omitting these tracts because the spatial pattern of
exclusion is itself analytically significant, revealing which
communities lack access to short term rental economic opportunities. The
Yellow Orange Red scheme provides intuitive low to high encoding
accessible to colorblind readers through luminance variation.
I calculated percentage non-Hispanic white population
(B03002_003E/B03002_001E×100) via tidycensus, standardizing by census
tract population to ensure comparability across varying tract sizes.
Median household income came from provided ACS data (B19013_001E). These
variables were selected because gentrification research demonstrates
Airbnb concentrates in neighborhoods with particular racial-economic
compositions (Wachsmuth & Weisler, 2018; Zhang and J.C. Fu, 2022).
Figure 3 employs quantile classification, selected over Jenks
because while Jenks revealed extreme inequality (bottom category
spanning $7,000 to $66,416), quantile ensures balanced visual
representation with each quintile representing 20% of tracts. Higher
white percentages (over 50%) concentrate in northern and centre west
coastal areas, while southern areas show predominantly lower percentages
(below 11%). Income patterns substantially overlap: centre west coastal
and northern areas display highest incomes ($120,000 to $250,000), while
centre south falls into the lowest bracket ($7,000 to $60,000),
coinciding with lowest white percentages. White or gray areas in both
maps indicate 45 census tracts with missing ACS data, of which 21 have
zero population and 29 have fewer than 100 residents. These might
represent uninhabited zones such as National Forest, military bases or
industrial areas without census records due to privacy concerns or lack
of residential population. Displaying these missing data areas as
distinct gray shading reveals the geographic extent of unpopulated zones
and prevents misleading interpolation across uninhabited territory. This
spatial correlation potentially reflects LA’s residential segregation
history and the fact that income is positively correlated to race even
today. The use of divergent color schemes (Purples for race, Greens for
income) aim to distinguish variables while maintaining clarity.
Figure 4’s bivariate choropleth combines ln(price) and percentage
white using quantile classification (3×3 grid), which normalizes both
variables. Natural log transformation compresses the $18 to $2,573 range
into an interpretable scale emphasizing proportional differences. Centre
north and centre west coastal areas display dark colors (high on both
dimensions), while southern tracts show light colors (low on both).
Areas with high prices but low white percentages are present in pockets
of the north and extremely few regions in the south. However, given the
very few observations in the north, this might be spurious correlations,
suggesting that there is a positive correlation between airbnb price and
percentage white.
This pattern reflects interconnected
explanations. First, non White hosts charge 7.58% less than White hosts
in LA (Jaeger and Sleegers, 2022), so predominantly white neighborhoods
with more White hosts may command higher prices through racial pricing
disparities. Second, building on Figure 3’s demonstrated race income
correlation (r equals 0.601), expensive coastal Airbnbs might reflect
both racial composition and neighborhood wealth, amenities, and tourist
infrastructure. This dual mechanism aligns with findings that
discrimination persists across diverse and homogeneous neighborhoods
(Edelman, Luca and Svirsky, 2015), indicating host race and neighborhood
characteristics independently influence pricing.
I queried 369 Metro/light rail stations via OSM, capturing
LA’s Red Line subway and Blue, Green, Gold, Expo, Purple light rail
lines (Anderson, 2014). Figure 5’s kernel density heatmap
(bandwidth=15km) reveals highest station density in centre-south
downtown, declining concentrically outward through high, medium, and low
density rings. This pattern reflects converging lines creating
downtown’s dense node, while vast areas on the peripheries and the coast
have minimal rail coverage, reflecting LA’s polycentric structure where
proximity to the city center does not significantly affect Airbnb
supply, especially given the high supply on the centre-west portion
which has seemingly low transport density (Zhang and J.C. Fu, 2022).
Figure 6 examines Downtown LA, where 225 of 861 listings (26%)
fall within 200 meter buffers of transit stations, with remaining
listings clustering near transit corridors. This demonstrates transit
accessibility correlates with short term rental locations in tourist
heavy mixed use areas. However, the modest 26% proximity rate suggests
other factors also shape distribution. This contrasts with denser cities
where short term renters show flatter distance gradients from city
centers (Coles et al., 2017), possibly because LA’s automobile culture
reduces transit’s relative importance for tourist accommodations.
I employed scatterplot analysis examining average Airbnb prices
against four socioeconomic variables across LA neighborhoods,
quantifying the spatial relationships observed in Figures 2-4.
Aggregating to neighborhood level facilitates comparison with literature
typically analyzing neighborhoods as units (Coles et al., 2017).
Figure 7 reveals positive correlations between prices and both
percentage white (r=0.602) and median income (r=0.636), indicating
premiums in wealthier, predominantly white neighborhoods. Similar
correlation strengths reflect substantial race-class overlap (r=0.60 in
correlation matrix). Santa Monica (on the centre west coast) exemplifies
this pattern with high values on both dimensions and elevated prices
(~$350), while Downtown shows lower white percentage (30%) with moderate
prices ($250). Hollywood Hills West emerges as an outlier with very high
prices despite moderate white percentages, likely reflecting celebrity
cachet and luxury housing rather than demographics alone.
The
negative transit correlation (r=-0.27) reveals that surprisingly, dense
transport networks do not create an airbnb premium and are in fact
negatively associated with it. This might be because of two factors : a)
that there are so many airbnbs present in areas of high density transit
(centre south) that it proves a challenge to charge premium prices, or
b) since public transport is not as widely used in certain airbnb dense
regions in LA, such as centre-west regions like Santa Monica, which are
well to do and more car-dependent with lower transport network density
thus leading to a negative correlation. This could potentially evidence
the fact that LA’s transit infrastructure disproportionately serves
tourism rather than access to mobility-dependent communities in the
centre-south, although a weak correlation does not provide strong enough
evidence to this fact. Education shows positive correlation (r=0.532),
supporting findings that highly educated neighborhoods were initial
hotspots (Coles et al., 2017), though weaker than race or income as
predictors.
Visualization employed scatterplots with linear
trend lines, highlighting three key neighborhoods (Santa Monica,
Downtown, Hollywood Hills West) as labeled points to connect abstract
correlations to concrete places analyzed throughout the report. The
correlation matrix reveals that race and education correlate very
strongly (r equals 0.777), indicating educational attainment itself
could be racialized in LA’s geography.
To examine whether crime shapes Airbnb distribution beyond
demographics, I incorporated 13,607 violent crime incidents (homicide,
rape, robbery, aggravated assault) from LAPD data (January
2024-September 2025). Recent empirical research establishes causal
Airbnb-crime links, though findings vary. O’Brien and Heydari found
Boston Airbnb density predicted violence increases after one year,
because large population of unknown, temporary members begin to
undermine neighborhood social organization (Ke, O’brien and Heydari,
2020). London evidence showed mixed results with positive correlations
with property crime but negative correlations with violent crime
(Maldonado-Guzmán, Francisco José Chamizo-Nieto and Reyes-Corredera,
2024).
Figure 8 presents side-by-side choropleths comparing
crime distribution (continuous scale using square-root transformation to
handle skew) with binary Airbnb density reflecting <50 vs. 50+
listings per tract, capturing dense tourist spots vs not so dense spots.
Spatial analysis reveals partial overlap rather than systematic
correlation. Highest Airbnb density concentrates in centre-west coastal
areas with low crime, while violent crime concentrates in centre and
centre-south areas where Airbnb density varies. Statistical analysis
confirms weak relationships: prices correlate negatively with crime
counts (r=-0.183) and rates (r=-0.081), indicating modest price
penalties in higher-crime areas, while Airbnb counts show virtually no
correlation with crime (r=0.019).
This weak crime Airbnb
relationship contrasts sharply with stronger race (r equals 0.494) and
income (r equals 0.504) correlations from Figure 7, suggesting
demographic composition matters substantially more than crime for Airbnb
distribution. The finding challenges simplistic crime deters tourists
narratives. It also does not provide enough evidence to support
arguments that an increase in Airbnb density could potentially increase
crime through social disorganization, although testing this accurately
would require longitudinal data unavailable in this cross sectional
analysis. The pattern also suggests centre south areas experiencing both
crime and Airbnb reflect confounding urban centrality where busy city
centers naturally have both tourism and crime independent of causal
relationships (Maldonado-Guzmán, Francisco José Chamizo-Nieto and
Reyes-Corredera, 2024). The binary classification for Airbnb (50 plus
threshold) was selected based on distribution analysis showing 95% of
tracts have fewer than 50 listings, with over half having 10 or fewer,
making 50 plus a meaningful high density designation distinguishing
genuine tourist concentration zones from areas with sparse or moderate
short term rental presence.
This spatial analysis of Los Angeles County reveals that Airbnb
distribution patterns might systematically reinforce rather than disrupt
existing urban inequalities. Short term rentals concentrate in wealthy,
predominantly white coastal neighborhoods (Santa Monica, Malibu,
Westside), commanding premium prices while remaining sparse or absent in
lower income communities of color (South LA, East LA). The moderate to
strong correlations between Airbnb prices and both racial composition (r
equals 0.494) and median income (r equals 0.504) demonstrate that
demographic factors could be large determiners of where tourism economy
benefits flow.
Three key findings emerge. First, the race
income Airbnb nexus operates consistently across LA’s geography, with
rare exceptions like Hollywood Hills West. Second, transit
infrastructure, while not so prevalent across the whole county, tends to
serve areas of high airbnb density and low prices, potentially aiming to
serve tourists rather than transit dependent communities, as evidenced
by negative correlation between Airbnb prices and resident transit use
(r equals negative 0.175). However, the weak correlation suggests weak
evidence and it could also be that the rest of LA is a lot more
car-dependent leading to lower public transport density. Third, crime
shows weak influence on Airbnb distribution (r equals negative 0.18),
suggesting safety concerns matter less than other potential factors like
race and perceptions of neighborhood desirability as well as centrality
to the city.
For LA decision makers, these patterns indicate
that market driven short term rental activity concentrates high priced
airbnbs in already privileged neighborhoods, while they flood lower
income neighbourhoods with a higher number of cheaper rentals. The
strong demographic correlations compared to weak crime and transit
effects suggest that policies addressing Airbnb’s inequitable impacts
must directly target structural racial and economic inequalities through
affordable housing protections, community benefit agreements, and
support for local entrepreneurship in historically marginalized
communities, rather than assuming infrastructure investments or crime
reduction alone will democratize tourism access. While these figures
reflect correlation and not causation, the analysis demonstrates how
short term rental platforms intersect with and potentially amplify long
standing patterns of residential segregation and uneven development in
Los Angeles.
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